Overview

Dataset statistics

Number of variables11
Number of observations1000000
Missing cells0
Missing cells (%)0.0%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory91.6 MiB
Average record size in memory96.0 B

Variable types

Numeric9
Categorical2

Alerts

Dataset has 1 (< 0.1%) duplicate rowsDuplicates
400kmDensity is highly overall correlated with SYM/H_INDEX_nT and 5 other fieldsHigh correlation
SYM/H_INDEX_nT is highly overall correlated with 400kmDensityHigh correlation
1-M_AE_nT is highly overall correlated with SYM/H_INDEX_nTHigh correlation
DAILY_SUNSPOT_NO_ is highly overall correlated with 400kmDensity and 4 other fieldsHigh correlation
DAILY_F10.7_ is highly overall correlated with 400kmDensity and 4 other fieldsHigh correlation
SOLAR_LYMAN-ALPHA_W/m^2 is highly overall correlated with 400kmDensity and 4 other fieldsHigh correlation
mg_index (core to wing ratio (unitless)) is highly overall correlated with 400kmDensity and 4 other fieldsHigh correlation
irradiance (W/m^2/nm) is highly overall correlated with 400kmDensity and 4 other fieldsHigh correlation
storm is highly overall correlated with storm phaseHigh correlation
storm phase is highly overall correlated with stormHigh correlation
SYM/H_INDEX_nT has 26867 (2.7%) zerosZeros
DAILY_SUNSPOT_NO_ has 241799 (24.2%) zerosZeros
d_diff has 15609 (1.6%) zerosZeros

Reproduction

Analysis started2023-02-24 21:26:56.000978
Analysis finished2023-02-24 21:27:50.457400
Duration54.46 seconds
Software versionydata-profiling vv4.0.0
Download configurationconfig.json

Variables

400kmDensity
Real number (ℝ)

Distinct944165
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4843981 × 10-12
Minimum4.683731 × 10-16
Maximum2.63181 × 10-11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 MiB
2023-02-24T16:27:50.547187image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum4.683731 × 10-16
5-th percentile2.034924 × 10-13
Q14.9719968 × 10-13
median9.6346285 × 10-13
Q31.931875 × 10-12
95-th percentile4.6191106 × 10-12
Maximum2.63181 × 10-11
Range2.6317632 × 10-11
Interquartile range (IQR)1.4346753 × 10-12

Descriptive statistics

Standard deviation1.4695661 × 10-12
Coefficient of variation (CV)0.9900081
Kurtosis0
Mean1.4843981 × 10-12
Median Absolute Deviation (MAD)5.7707505 × 10-13
Skewness0
Sum1.4843981 × 10-6
Variance2.1596246 × 10-24
MonotonicityNot monotonic
2023-02-24T16:27:50.679832image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.12497 × 10-125
 
< 0.1%
1.398329 × 10-125
 
< 0.1%
1.000263 × 10-125
 
< 0.1%
1.347624 × 10-125
 
< 0.1%
1.149195 × 10-125
 
< 0.1%
1.31223 × 10-125
 
< 0.1%
1.073999 × 10-125
 
< 0.1%
1.062096 × 10-125
 
< 0.1%
1.207641 × 10-125
 
< 0.1%
1.3132 × 10-125
 
< 0.1%
Other values (944155) 999950
> 99.9%
ValueCountFrequency (%)
4.683731 × 10-161
< 0.1%
4.738029 × 10-161
< 0.1%
5.77655 × 10-161
< 0.1%
6.499741 × 10-161
< 0.1%
6.652488 × 10-162
< 0.1%
7.823372 × 10-161
< 0.1%
1.02887 × 10-151
< 0.1%
1.189884 × 10-151
< 0.1%
1.367567 × 10-151
< 0.1%
1.503559 × 10-151
< 0.1%
ValueCountFrequency (%)
2.63181 × 10-111
< 0.1%
2.524422 × 10-111
< 0.1%
2.4308 × 10-111
< 0.1%
2.36808 × 10-111
< 0.1%
2.303079 × 10-111
< 0.1%
2.255126 × 10-111
< 0.1%
2.238326 × 10-111
< 0.1%
2.217619 × 10-111
< 0.1%
2.217417 × 10-111
< 0.1%
2.180779 × 10-111
< 0.1%

SYM/H_INDEX_nT
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct511
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-11.418843
Minimum-486
Maximum151
Zeros26867
Zeros (%)2.7%
Negative765096
Negative (%)76.5%
Memory size15.3 MiB
2023-02-24T16:27:50.806493image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-486
5-th percentile-41
Q1-18
median-8
Q3-1
95-th percentile9
Maximum151
Range637
Interquartile range (IQR)17

Descriptive statistics

Standard deviation19.003924
Coefficient of variation (CV)-1.66426
Kurtosis48.839113
Mean-11.418843
Median Absolute Deviation (MAD)8
Skewness-4.083699
Sum-11418843
Variance361.14913
MonotonicityNot monotonic
2023-02-24T16:27:50.921159image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-3 35846
 
3.6%
-4 35103
 
3.5%
-2 34567
 
3.5%
-5 34358
 
3.4%
-7 33921
 
3.4%
-6 33287
 
3.3%
-8 33182
 
3.3%
-1 32500
 
3.2%
-9 32386
 
3.2%
-10 30228
 
3.0%
Other values (501) 664622
66.5%
ValueCountFrequency (%)
-486 1
< 0.1%
-482 1
< 0.1%
-480 1
< 0.1%
-479 1
< 0.1%
-473 1
< 0.1%
-471 1
< 0.1%
-468 1
< 0.1%
-466 1
< 0.1%
-465 1
< 0.1%
-461 2
< 0.1%
ValueCountFrequency (%)
151 1
< 0.1%
143 1
< 0.1%
134 1
< 0.1%
127 1
< 0.1%
115 1
< 0.1%
111 1
< 0.1%
110 1
< 0.1%
108 1
< 0.1%
106 1
< 0.1%
103 1
< 0.1%

1-M_AE_nT
Real number (ℝ)

Distinct2100
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean176.47598
Minimum1
Maximum3680
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 MiB
2023-02-24T16:27:51.051837image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile15
Q139
median88
Q3236
95-th percentile620
Maximum3680
Range3679
Interquartile range (IQR)197

Descriptive statistics

Standard deviation214.26228
Coefficient of variation (CV)1.2141158
Kurtosis9.7629609
Mean176.47598
Median Absolute Deviation (MAD)62
Skewness2.4973977
Sum1.7647598 × 108
Variance45908.326
MonotonicityNot monotonic
2023-02-24T16:27:51.172515image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 9092
 
0.9%
34 8958
 
0.9%
36 8942
 
0.9%
35 8925
 
0.9%
27 8892
 
0.9%
31 8880
 
0.9%
33 8877
 
0.9%
32 8859
 
0.9%
37 8822
 
0.9%
26 8791
 
0.9%
Other values (2090) 910962
91.1%
ValueCountFrequency (%)
1 26
 
< 0.1%
2 200
 
< 0.1%
3 537
 
0.1%
4 1180
 
0.1%
5 1860
 
0.2%
6 2738
0.3%
7 3358
0.3%
8 3996
0.4%
9 4564
0.5%
10 5093
0.5%
ValueCountFrequency (%)
3680 1
< 0.1%
3642 1
< 0.1%
3632 1
< 0.1%
3560 1
< 0.1%
3549 1
< 0.1%
3420 1
< 0.1%
3380 1
< 0.1%
3361 1
< 0.1%
3360 1
< 0.1%
3354 1
< 0.1%

DAILY_SUNSPOT_NO_
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct214
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.51765
Minimum0
Maximum281
Zeros241799
Zeros (%)24.2%
Negative0
Negative (%)0.0%
Memory size15.3 MiB
2023-02-24T16:27:51.300173image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18
median31
Q373
95-th percentile149
Maximum281
Range281
Interquartile range (IQR)65

Descriptive statistics

Standard deviation50.38335
Coefficient of variation (CV)1.0603081
Kurtosis1.4280014
Mean47.51765
Median Absolute Deviation (MAD)31
Skewness1.2957918
Sum47517650
Variance2538.4819
MonotonicityNot monotonic
2023-02-24T16:27:51.412873image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 241799
 
24.2%
13 24294
 
2.4%
12 23958
 
2.4%
15 19696
 
2.0%
14 18350
 
1.8%
18 14633
 
1.5%
16 13501
 
1.4%
26 13351
 
1.3%
11 11951
 
1.2%
23 9877
 
1.0%
Other values (204) 608590
60.9%
ValueCountFrequency (%)
0 241799
24.2%
5 625
 
0.1%
6 1575
 
0.2%
7 3369
 
0.3%
8 2755
 
0.3%
9 4806
 
0.5%
10 8054
 
0.8%
11 11951
 
1.2%
12 23958
 
2.4%
13 24294
 
2.4%
ValueCountFrequency (%)
281 288
< 0.1%
279 294
< 0.1%
270 309
< 0.1%
267 271
< 0.1%
263 285
< 0.1%
252 296
< 0.1%
250 565
0.1%
248 618
0.1%
247 649
0.1%
239 293
< 0.1%

DAILY_F10.7_
Real number (ℝ)

Distinct927
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean97.638715
Minimum65.1
Maximum999.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 MiB
2023-02-24T16:27:51.537539image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum65.1
5-th percentile67.5
Q171.5
median85.2
Q3111.1
95-th percentile158
Maximum999.9
Range934.8
Interquartile range (IQR)39.6

Descriptive statistics

Standard deviation54.592442
Coefficient of variation (CV)0.559127
Kurtosis184.60353
Mean97.638715
Median Absolute Deviation (MAD)15.6
Skewness11.559653
Sum97638715
Variance2980.3347
MonotonicityNot monotonic
2023-02-24T16:27:51.659240image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
69.3 8067
 
0.8%
68 7585
 
0.8%
69.5 7226
 
0.7%
68.8 7077
 
0.7%
69.8 7074
 
0.7%
67.4 6458
 
0.6%
67.8 6306
 
0.6%
70.3 6174
 
0.6%
68.2 6141
 
0.6%
70.5 6134
 
0.6%
Other values (917) 931758
93.2%
ValueCountFrequency (%)
65.1 315
 
< 0.1%
65.2 293
 
< 0.1%
65.5 305
 
< 0.1%
65.6 311
 
< 0.1%
65.8 607
 
0.1%
65.9 618
 
0.1%
66 959
 
0.1%
66.1 890
 
0.1%
66.2 2418
0.2%
66.3 2089
0.2%
ValueCountFrequency (%)
999.9 2507
0.3%
275.4 288
 
< 0.1%
270.9 331
 
< 0.1%
267.6 277
 
< 0.1%
254 324
 
< 0.1%
246.9 309
 
< 0.1%
245.2 293
 
< 0.1%
242.6 299
 
< 0.1%
240.6 303
 
< 0.1%
232.8 269
 
< 0.1%

SOLAR_LYMAN-ALPHA_W/m^2
Real number (ℝ)

Distinct1687
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0068248357
Minimum0.00588
Maximum0.009751
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 MiB
2023-02-24T16:27:51.786872image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.00588
5-th percentile0.005983
Q10.006174
median0.006577
Q30.007318
95-th percentile0.008347
Maximum0.009751
Range0.003871
Interquartile range (IQR)0.001144

Descriptive statistics

Standard deviation0.000778208
Coefficient of variation (CV)0.11402589
Kurtosis0.39401568
Mean0.0068248357
Median Absolute Deviation (MAD)0.000513
Skewness0.98620879
Sum6824.8357
Variance6.0560769 × 10-7
MonotonicityNot monotonic
2023-02-24T16:27:51.911539image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.00602 3133
 
0.3%
0.006027 3127
 
0.3%
0.005978 2786
 
0.3%
0.005991 2779
 
0.3%
0.006047 2715
 
0.3%
0.005961 2499
 
0.2%
0.006 2482
 
0.2%
0.006019 2481
 
0.2%
0.006071 2480
 
0.2%
0.006006 2473
 
0.2%
Other values (1677) 973045
97.3%
ValueCountFrequency (%)
0.00588 270
 
< 0.1%
0.005897 293
 
< 0.1%
0.005898 325
 
< 0.1%
0.005904 317
 
< 0.1%
0.005907 624
0.1%
0.005908 300
 
< 0.1%
0.005909 322
 
< 0.1%
0.00591 911
0.1%
0.005912 295
 
< 0.1%
0.005913 318
 
< 0.1%
ValueCountFrequency (%)
0.009751 269
< 0.1%
0.00974 299
< 0.1%
0.00972 314
< 0.1%
0.009662 309
< 0.1%
0.009581 320
< 0.1%
0.009577 288
< 0.1%
0.009555 316
< 0.1%
0.00954 331
< 0.1%
0.009511 333
< 0.1%
0.009483 285
< 0.1%
Distinct2312
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.26856323
Minimum0.26295999
Maximum0.28494
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 MiB
2023-02-24T16:27:52.052135image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.26295999
5-th percentile0.26374638
Q10.26470664
median0.267036
Q30.27149001
95-th percentile0.27770001
Maximum0.28494
Range0.02198001
Interquartile range (IQR)0.00678337

Descriptive statistics

Standard deviation0.0045693352
Coefficient of variation (CV)0.017014001
Kurtosis0.15074743
Mean0.26856323
Median Absolute Deviation (MAD)0.00271005
Skewness0.9729111
Sum268563.23
Variance2.0878824 × 10-5
MonotonicityNot monotonic
2023-02-24T16:27:52.171843image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.26708001 2792
 
0.3%
0.26403001 2479
 
0.2%
0.26418999 2466
 
0.2%
0.26475999 2167
 
0.2%
0.26532999 2143
 
0.2%
0.26469001 2133
 
0.2%
0.26363 1930
 
0.2%
0.26451999 1887
 
0.2%
0.26370001 1860
 
0.2%
0.26482001 1831
 
0.2%
Other values (2302) 978312
97.8%
ValueCountFrequency (%)
0.26295999 338
< 0.1%
0.26299 294
< 0.1%
0.26300001 291
< 0.1%
0.26304999 314
< 0.1%
0.26306999 311
< 0.1%
0.26308 296
< 0.1%
0.26309001 322
< 0.1%
0.26311001 310
< 0.1%
0.26312 295
< 0.1%
0.26313001 630
0.1%
ValueCountFrequency (%)
0.28494 299
< 0.1%
0.28485999 269
< 0.1%
0.28428999 309
< 0.1%
0.28426999 314
< 0.1%
0.2841 288
< 0.1%
0.28386 316
< 0.1%
0.28376999 331
< 0.1%
0.28373272 307
< 0.1%
0.28373 295
< 0.1%
0.28360999 335
< 0.1%

irradiance (W/m^2/nm)
Real number (ℝ)

Distinct3233
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0055147674
Minimum0.0048730583
Maximum0.0073493496
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 MiB
2023-02-24T16:27:52.291496image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0048730583
5-th percentile0.0049231751
Q10.0050500929
median0.0053276089
Q30.005854608
95-th percentile0.006616591
Maximum0.0073493496
Range0.0024762913
Interquartile range (IQR)0.00080451509

Descriptive statistics

Standard deviation0.00054606932
Coefficient of variation (CV)0.099019466
Kurtosis0.12758826
Mean0.0055147674
Median Absolute Deviation (MAD)0.00035874778
Skewness0.92997418
Sum5514.7674
Variance2.9819171 × 10-7
MonotonicityNot monotonic
2023-02-24T16:27:52.416190image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.00495163817 943
 
0.1%
0.00491212029 942
 
0.1%
0.004914534744 925
 
0.1%
0.005085965153 655
 
0.1%
0.005003940314 654
 
0.1%
0.005974987987 651
 
0.1%
0.004940411076 649
 
0.1%
0.00519876834 647
 
0.1%
0.004899295513 637
 
0.1%
0.004931729753 637
 
0.1%
Other values (3223) 992660
99.3%
ValueCountFrequency (%)
0.004873058293 292
< 0.1%
0.004877128173 307
< 0.1%
0.004877185915 291
< 0.1%
0.004877588246 315
< 0.1%
0.004881324712 166
< 0.1%
0.004881698173 151
< 0.1%
0.004881755915 325
< 0.1%
0.00488556223 325
< 0.1%
0.004885710776 302
< 0.1%
0.004885739647 299
< 0.1%
ValueCountFrequency (%)
0.007349349558 266
< 0.1%
0.00734248152 298
< 0.1%
0.007334709167 341
< 0.1%
0.007301890757 302
< 0.1%
0.007268224377 332
< 0.1%
0.007266042288 310
< 0.1%
0.007259562146 306
< 0.1%
0.007257604506 299
< 0.1%
0.007247306872 324
< 0.1%
0.007218547165 320
< 0.1%

storm
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size15.3 MiB
1
561171 
-1
438829 

Length

Max length2
Median length1
Mean length1.438829
Min length1

Characters and Unicode

Total characters1438829
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row-1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 561171
56.1%
-1 438829
43.9%

Length

2023-02-24T16:27:52.528887image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-24T16:27:52.640563image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1 1000000
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1000000
69.5%
- 438829
30.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1000000
69.5%
Dash Punctuation 438829
30.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1000000
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 438829
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1438829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1000000
69.5%
- 438829
30.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1438829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1000000
69.5%
- 438829
30.5%

storm phase
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size15.3 MiB
-1
438829 
2
339793 
1
221378 

Length

Max length2
Median length1
Mean length1.438829
Min length1

Characters and Unicode

Total characters1438829
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row-1
4th row2
5th row2

Common Values

ValueCountFrequency (%)
-1 438829
43.9%
2 339793
34.0%
1 221378
22.1%

Length

2023-02-24T16:27:52.724339image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-24T16:27:52.824072image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1 660207
66.0%
2 339793
34.0%

Most occurring characters

ValueCountFrequency (%)
1 660207
45.9%
- 438829
30.5%
2 339793
23.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1000000
69.5%
Dash Punctuation 438829
30.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 660207
66.0%
2 339793
34.0%
Dash Punctuation
ValueCountFrequency (%)
- 438829
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1438829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 660207
45.9%
- 438829
30.5%
2 339793
23.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1438829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 660207
45.9%
- 438829
30.5%
2 339793
23.6%

d_diff
Real number (ℝ)

Distinct834454
Distinct (%)83.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.1026985 × 10-16
Minimum-1.1631809 × 10-11
Maximum1.00712 × 10-11
Zeros15609
Zeros (%)1.6%
Negative480785
Negative (%)48.1%
Memory size15.3 MiB
2023-02-24T16:27:52.932781image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-1.1631809 × 10-11
5-th percentile-1.6507727 × 10-13
Q1-3.626315 × 10-14
median4.299 × 10-16
Q33.7830775 × 10-14
95-th percentile1.6050301 × 10-13
Maximum1.00712 × 10-11
Range2.1703009 × 10-11
Interquartile range (IQR)7.4093925 × 10-14

Descriptive statistics

Standard deviation1.676714 × 10-13
Coefficient of variation (CV)-1520.5552
Kurtosis0
Mean-1.1026985 × 10-16
Median Absolute Deviation (MAD)3.706725 × 10-14
Skewness0
Sum-1.1026985 × 10-10
Variance2.8113697 × 10-26
MonotonicityNot monotonic
2023-02-24T16:27:53.053487image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 15609
 
1.6%
-1.0598 × 10-1410
 
< 0.1%
4.0388 × 10-1410
 
< 0.1%
-1.361 × 10-149
 
< 0.1%
-7.93 × 10-159
 
< 0.1%
2.8602 × 10-149
 
< 0.1%
7.229 × 10-159
 
< 0.1%
1.6732 × 10-149
 
< 0.1%
-1.8985 × 10-149
 
< 0.1%
-1.9919 × 10-149
 
< 0.1%
Other values (834444) 984308
98.4%
ValueCountFrequency (%)
-1.163180901 × 10-111
< 0.1%
-6.7099063 × 10-121
< 0.1%
-6.6593034 × 10-121
< 0.1%
-6.5239223 × 10-121
< 0.1%
-6.3550716 × 10-121
< 0.1%
-6.35446 × 10-121
< 0.1%
-6.277296 × 10-121
< 0.1%
-6.136109 × 10-121
< 0.1%
-6.02464 × 10-121
< 0.1%
-5.9807251 × 10-121
< 0.1%
ValueCountFrequency (%)
1.00712 × 10-111
< 0.1%
9.4953338 × 10-121
< 0.1%
8.5252 × 10-121
< 0.1%
7.728122 × 10-121
< 0.1%
7.166516 × 10-121
< 0.1%
7.0179446 × 10-121
< 0.1%
6.6253107 × 10-121
< 0.1%
6.5796192 × 10-121
< 0.1%
6.497051 × 10-121
< 0.1%
6.3993213 × 10-121
< 0.1%

Interactions

2023-02-24T16:27:46.886927image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:31.799379image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:33.752046image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:35.697843image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:37.587788image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:39.418898image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:41.318825image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:43.222722image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:45.061809image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:47.083401image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:32.044724image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:34.062185image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:35.914262image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:37.792217image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:39.621353image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:41.531245image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:43.427148image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:45.264264image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:47.285866image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:32.265019image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:34.262654image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:36.117718image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:37.998697image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:39.823785image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:41.745644image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:43.639608image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:45.472707image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:47.492279image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:32.490391image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:34.471124image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:36.334111image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:38.203142image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:40.134978image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:41.961098image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:43.847060image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:45.684149image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:47.694739image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:32.706847image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:34.672581image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:36.544593image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:38.403579image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:40.325477image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:42.174525image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:44.050512image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:45.886605image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:47.888223image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:32.921236image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:34.877036image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:36.745017image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:38.603082image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:40.519952image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:42.372997image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:44.249949image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:46.085074image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:48.249284image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:33.151620image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:35.094426image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:36.965456image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:38.817500image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:40.727402image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:42.589421image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:44.459388image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:46.295481image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:48.450745image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:33.354110image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:35.295915image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:37.170904image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:39.021954image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:40.926833image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:42.801848image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:44.656897image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:46.492969image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:48.643231image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:33.556570image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:35.496382image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:37.380348image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:39.222421image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:41.123307image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:43.013273image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:44.859319image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:27:46.689458image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-02-24T16:27:53.157195image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
400kmDensitySYM/H_INDEX_nT1-M_AE_nTDAILY_SUNSPOT_NO_DAILY_F10.7_SOLAR_LYMAN-ALPHA_W/m^2mg_index (core to wing ratio (unitless))irradiance (W/m^2/nm)d_diffstormstorm phase
400kmDensity1.000-0.3050.3910.7710.8210.8490.8150.8560.0480.1510.107
SYM/H_INDEX_nT-0.3051.000-0.529-0.174-0.188-0.203-0.167-0.203-0.0040.1770.150
1-M_AE_nT0.391-0.5291.0000.3060.3240.3400.2840.3480.0060.2230.161
DAILY_SUNSPOT_NO_0.771-0.1740.3061.0000.9350.9060.8950.8940.0070.2170.160
DAILY_F10.7_0.821-0.1880.3240.9351.0000.9540.9380.9490.0080.0630.057
SOLAR_LYMAN-ALPHA_W/m^20.849-0.2030.3400.9060.9541.0000.9510.9920.0090.2330.170
mg_index (core to wing ratio (unitless))0.815-0.1670.2840.8950.9380.9511.0000.9430.0070.1980.144
irradiance (W/m^2/nm)0.856-0.2030.3480.8940.9490.9920.9431.0000.0090.2480.182
d_diff0.048-0.0040.0060.0070.0080.0090.0070.0091.0000.0430.030
storm0.1510.1770.2230.2170.0630.2330.1980.2480.0431.0001.000
storm phase0.1070.1500.1610.1600.0570.1700.1440.1820.0301.0001.000
2023-02-24T16:27:53.329748image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
400kmDensitySYM/H_INDEX_nT1-M_AE_nTDAILY_SUNSPOT_NO_DAILY_F10.7_SOLAR_LYMAN-ALPHA_W/m^2mg_index (core to wing ratio (unitless))irradiance (W/m^2/nm)stormstorm phased_diff
400kmDensity1.000-0.3470.3080.7360.4360.8180.7910.8270.2020.1980.056
SYM/H_INDEX_nT-0.3471.000-0.549-0.186-0.119-0.213-0.181-0.210-0.283-0.338-0.000
1-M_AE_nT0.308-0.5491.0000.2290.1320.2580.2120.2660.2800.2740.003
DAILY_SUNSPOT_NO_0.736-0.1860.2291.0000.5070.9040.8970.8840.1870.177-0.001
DAILY_F10.7_0.436-0.1190.1320.5071.0000.5240.5150.5150.1330.1320.000
SOLAR_LYMAN-ALPHA_W/m^20.818-0.2130.2580.9040.5241.0000.9630.9890.2120.203-0.001
mg_index (core to wing ratio (unitless))0.791-0.1810.2120.8970.5150.9631.0000.9490.1560.148-0.001
irradiance (W/m^2/nm)0.827-0.2100.2660.8840.5150.9890.9491.0000.2200.209-0.001
storm0.202-0.2830.2800.1870.1330.2120.1560.2201.0000.962-0.002
storm phase0.198-0.3380.2740.1770.1320.2030.1480.2090.9621.000-0.002
d_diff0.056-0.0000.003-0.0010.000-0.001-0.001-0.001-0.002-0.0021.000
2023-02-24T16:27:53.501289image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
400kmDensitySYM/H_INDEX_nT1-M_AE_nTDAILY_SUNSPOT_NO_DAILY_F10.7_SOLAR_LYMAN-ALPHA_W/m^2mg_index (core to wing ratio (unitless))irradiance (W/m^2/nm)stormstorm phased_diff
400kmDensity1.000-0.3050.3910.7710.8210.8490.8150.8560.2660.2550.048
SYM/H_INDEX_nT-0.3051.000-0.529-0.174-0.188-0.203-0.167-0.203-0.330-0.427-0.004
1-M_AE_nT0.391-0.5291.0000.3060.3240.3400.2840.3480.3180.3200.006
DAILY_SUNSPOT_NO_0.771-0.1740.3061.0000.9350.9060.8950.8940.2130.1890.007
DAILY_F10.7_0.821-0.1880.3240.9351.0000.9540.9380.9490.2280.2020.008
SOLAR_LYMAN-ALPHA_W/m^20.849-0.2030.3400.9060.9541.0000.9510.9920.2380.2150.009
mg_index (core to wing ratio (unitless))0.815-0.1670.2840.8950.9380.9511.0000.9430.1770.1560.007
irradiance (W/m^2/nm)0.856-0.2030.3480.8940.9490.9920.9431.0000.2420.2180.009
storm0.266-0.3300.3180.2130.2280.2380.1770.2421.0000.9240.001
storm phase0.255-0.4270.3200.1890.2020.2150.1560.2180.9241.0000.001
d_diff0.048-0.0040.0060.0070.0080.0090.0070.0090.0010.0011.000
2023-02-24T16:27:53.832406image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
400kmDensitySYM/H_INDEX_nT1-M_AE_nTDAILY_SUNSPOT_NO_DAILY_F10.7_SOLAR_LYMAN-ALPHA_W/m^2mg_index (core to wing ratio (unitless))irradiance (W/m^2/nm)stormstorm phased_diff
400kmDensity1.000-0.2110.2660.5760.6160.6480.6070.6580.2170.1970.034
SYM/H_INDEX_nT-0.2111.000-0.372-0.121-0.128-0.138-0.113-0.138-0.273-0.325-0.003
1-M_AE_nT0.266-0.3721.0000.2110.2200.2300.1900.2350.2600.2470.004
DAILY_SUNSPOT_NO_0.576-0.1210.2111.0000.7900.7390.7230.7210.1780.1500.005
DAILY_F10.7_0.616-0.1280.2200.7901.0000.8100.7790.7990.1870.1570.006
SOLAR_LYMAN-ALPHA_W/m^20.648-0.1380.2300.7390.8101.0000.8060.9260.1940.1670.006
mg_index (core to wing ratio (unitless))0.607-0.1130.1900.7230.7790.8061.0000.7850.1440.1220.005
irradiance (W/m^2/nm)0.658-0.1380.2350.7210.7990.9260.7851.0000.1980.1690.006
storm0.217-0.2730.2600.1780.1870.1940.1440.1981.0000.8750.001
storm phase0.197-0.3250.2470.1500.1570.1670.1220.1690.8751.0000.001
d_diff0.034-0.0030.0040.0050.0060.0060.0050.0060.0010.0011.000
2023-02-24T16:27:54.006937image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
400kmDensitySYM/H_INDEX_nT1-M_AE_nTDAILY_SUNSPOT_NO_DAILY_F10.7_SOLAR_LYMAN-ALPHA_W/m^2mg_index (core to wing ratio (unitless))irradiance (W/m^2/nm)stormstorm phased_diff
400kmDensity1.0000.6000.2870.6140.5070.6810.6630.6850.1970.1780.325
SYM/H_INDEX_nT0.6001.0000.4600.2920.2670.2180.2120.2210.2300.2440.119
1-M_AE_nT0.2870.4601.0000.2360.1380.2360.2230.2370.2900.2600.089
DAILY_SUNSPOT_NO_0.6140.2920.2361.0000.6920.8810.8580.8510.2830.2590.076
DAILY_F10.7_0.5070.2670.1380.6921.0000.6630.6280.6180.0950.0600.067
SOLAR_LYMAN-ALPHA_W/m^20.6810.2180.2360.8810.6631.0000.9540.9720.3040.2730.088
mg_index (core to wing ratio (unitless))0.6630.2120.2230.8580.6280.9541.0000.9240.2580.2350.077
irradiance (W/m^2/nm)0.6850.2210.2370.8510.6180.9720.9241.0000.3230.2910.088
storm0.1970.2300.2900.2830.0950.3040.2580.3231.0001.0000.043
storm phase0.1780.2440.2600.2590.0600.2730.2350.2911.0001.0000.070
d_diff0.3250.1190.0890.0760.0670.0880.0770.0880.0430.0701.000
2023-02-24T16:27:54.156543image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
stormstorm phase
storm1.0001.000
storm phase1.0001.000

Missing values

2023-02-24T16:27:48.789839image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-24T16:27:49.291469image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

400kmDensitySYM/H_INDEX_nT1-M_AE_nTDAILY_SUNSPOT_NO_DAILY_F10.7_SOLAR_LYMAN-ALPHA_W/m^2mg_index (core to wing ratio (unitless))irradiance (W/m^2/nm)stormstorm phased_diff
22066321.104221e-12-51.0257.077.098.10.0070600.2691800.00567412-9.363700e-14
10693443.525742e-13-23.078.016.072.60.0063880.2645200.005207122.512110e-14
44160148.036335e-134.027.033.086.30.0066450.2675000.005432-1-12.999230e-14
18339333.256402e-13-10.0238.011.067.40.0061020.2644140.005023121.834770e-14
37642601.981406e-12-34.0522.065.0116.60.0075240.2721600.00593612-3.181240e-13
29897345.017889e-13-31.0179.015.065.10.0060790.2635200.004981125.317890e-14
45445201.086670e-12-22.0427.015.079.90.0064110.2658200.00529911-1.180550e-13
34380591.122827e-12-17.034.022.084.60.0066650.2667200.00545812-2.714500e-14
10345254.917163e-1314.065.00.069.40.0061770.2637500.005062121.839200e-15
44146696.219013e-13-5.0252.065.086.50.0067520.2681500.005441-1-1-9.447090e-14
400kmDensitySYM/H_INDEX_nT1-M_AE_nTDAILY_SUNSPOT_NO_DAILY_F10.7_SOLAR_LYMAN-ALPHA_W/m^2mg_index (core to wing ratio (unitless))irradiance (W/m^2/nm)stormstorm phased_diff
33942168.820319e-13-5.0132.021.089.60.0067020.2670800.00529212-2.216251e-13
17802382.042599e-13-2.014.00.075.50.0061470.2656280.005064-1-19.425400e-15
35701321.873112e-133.037.023.072.90.0062150.2641000.005079-1-1-8.847500e-15
26393186.676092e-13-9.042.063.0102.50.0069380.2704740.005624-1-11.624300e-14
30794522.409493e-12-18.0312.089.0114.70.0078910.2737000.00621712-7.433400e-14
27972892.265046e-12-55.0697.054.0102.00.0074120.2746100.00595911-2.068140e-13
31761766.779453e-13-9.018.012.067.90.0060810.2633600.004962-1-1-4.580640e-14
25230891.075160e-124.051.012.075.40.0063260.2668970.005166-1-12.428500e-14
20744861.473038e-12-28.0411.0118.0137.20.0076770.2753650.00609212-1.351930e-13
9749351.382268e-123.0214.037.077.90.0064300.2656500.005298121.019720e-13

Duplicate rows

Most frequently occurring

400kmDensitySYM/H_INDEX_nT1-M_AE_nTDAILY_SUNSPOT_NO_DAILY_F10.7_SOLAR_LYMAN-ALPHA_W/m^2mg_index (core to wing ratio (unitless))irradiance (W/m^2/nm)stormstorm phased_diff# duplicates
09.262667e-14-4.029.00.070.20.0060060.263910.005002-1-10.02